A Recommender System-Inspired Cloud Data Filling Scheme for Satellite-based Coastal Observation

• URL: http://arxiv.org/abs/2111.13955v1
• Date: Sat, 27 Nov 2021 18:26:11 GMT
• Title: A Recommender System-Inspired Cloud Data Filling Scheme for Satellite-based Coastal Observation
• Authors: Ruo-Qian Wang
• Abstract summary: This study is inspired by the success of data imputation methods in recommender systems that are designed for online shopping. A numerical experiment was designed and conducted for a LandSat dataset with a range of synthetic cloud covers. The recommender system-inspired matrix factorization algorithm called Funk-SVD showed superior performance in computational accuracy and efficiency.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Filling missing data in cloud-covered areas of satellite imaging is an important task to improve data quantity and quality for enhanced earth observation. Traditional cloud filling studies focused on continuous numerical data such as temperature and cyanobacterial concentration in the open ocean. Cloud data filling issues in coastal imaging is far less studied because of the complex landscape. Inspired by the success of data imputation methods in recommender systems that are designed for online shopping, the present study explored their application to satellite cloud data filling tasks. A numerical experiment was designed and conducted for a LandSat dataset with a range of synthetic cloud covers to examine the performance of different data filling schemes. The recommender system-inspired matrix factorization algorithm called Funk-SVD showed superior performance in computational accuracy and efficiency for the task of recovering landscape types in a complex coastal area than the traditional data filling scheme of DINEOF (Data Interpolating Empirical Orthogonal Functions) and the deep learning method of Datawig. The new method achieved the best filling accuracy and reached a speed comparable to DINEOF and much faster than deep learning. A theoretical framework was created to analyze the error propagation in DINEOF and found the algorithm needs to be modified to converge to the ground truth. The present study showed that Funk-SVD has great potential to enhance cloud data filling performance and connects the fields of recommender systems and cloud filling to promote the improvement and sharing of useful algorithms.

Related papers

• Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image [24.09239785062109]
  We develop a novel dataset for accurate cloud recognition. We use domain adaptation methods to align 70,419 image-label pairs in terms of projection, temporal resolution, and spatial resolution. We also introduce a Distribution-aware Interactive-Attention Network (DIAnet), which preserves pixel-level details through a high-resolution branch and a parallel cross-branch.
  arXiv  Detail & Related papers  (2024-01-06T09:58:09Z)
• Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI [3.4764766275808583]
  Cloud formations often obscure optical satellite-based monitoring of the Earth's surface. We propose a novel synthetic dataset for cloud optical thickness estimation. We leverage for obtaining reliable and versatile cloud masks on real data.
  arXiv  Detail & Related papers  (2023-11-23T14:28:28Z)
• Dataset Distillation: A Comprehensive Review [76.26276286545284]
  dataset distillation (DD) aims to derive a much smaller dataset containing synthetic samples, based on which the trained models yield performance comparable with those trained on the original dataset. This paper gives a comprehensive review and summary of recent advances in DD and its application.
  arXiv  Detail & Related papers  (2023-01-17T17:03:28Z)
• A Comprehensive Survey of Dataset Distillation [73.15482472726555]
  It has become challenging to handle the unlimited growth of data with limited computing power. Deep learning technology has developed unprecedentedly in the last decade. This paper provides a holistic understanding of dataset distillation from multiple aspects.
  arXiv  Detail & Related papers  (2023-01-13T15:11:38Z)
• Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation [151.70234052015948]
  We propose a novel approach that encourages the optimization algorithm to seek a flat trajectory. We show that the weights trained on synthetic data are robust against the accumulated errors perturbations with the regularization towards the flat trajectory. Our method, called Flat Trajectory Distillation (FTD), is shown to boost the performance of gradient-matching methods by up to 4.7%.
  arXiv  Detail & Related papers  (2022-11-20T15:49:11Z)
• Segmentation-guided Domain Adaptation for Efficient Depth Completion [3.441021278275805]
  We propose an efficient depth completion model based on a vgg05-like CNN architecture and a semi-supervised domain adaptation approach. In order to boost spatial coherence, we guide the learning process using segmentations as additional source of information. Our approach improves on previous efficient and low parameter state of the art approaches while having a noticeably lower computational footprint.
  arXiv  Detail & Related papers  (2022-10-14T13:01:25Z)
• Learning Digital Terrain Models from Point Clouds: ALS2DTM Dataset and Rasterization-based GAN [6.267267911687337]
  This paper collects from open sources a large-scale dataset of ALS point clouds and corresponding DTMs with various urban, forested, and mountainous scenes. A baseline method is proposed as the first attempt to train a Deep neural network to extract digital Terrain models directly from ALS point clouds. Extensive studies with well-established methods are performed to benchmark the dataset and analyze the challenges in learning to extract DTM from point clouds.
  arXiv  Detail & Related papers  (2022-06-08T09:50:48Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• DeepSatData: Building large scale datasets of satellite images for training machine learning models [77.17638664503215]
  This report presents design considerations for automatically generating satellite imagery datasets for training machine learning models. We discuss issues faced from the point of view of deep neural network training and evaluation.
  arXiv  Detail & Related papers  (2021-04-28T15:13:12Z)
• Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery [15.459106705735376]
  The majority of optical observations acquired via spaceborne earth imagery are affected by clouds. We target the challenge of generalization by curating a large novel data set for training new cloud removal approaches. Based on the observation that cloud coverage varies widely between clear skies and absolute coverage, we propose a novel model that can deal with either extremes.
  arXiv  Detail & Related papers  (2020-09-16T13:40:42Z)
• Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision [68.35777836993212]
  We propose a Pseudo-LiDAR point cloud network to generate temporally and spatially high-quality point cloud sequences. By exploiting the scene flow between point clouds, the proposed network is able to learn a more accurate representation of the 3D spatial motion relationship.
  arXiv  Detail & Related papers  (2020-06-20T03:11:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.